About this role
<p> </p> <p><strong>Your job:</strong></p> <ul> <li>Develop, optimize, and deploy machine learning models for embedded and edge devices</li> <li>Ensure real-time performance, memory efficiency, and low-power operation of on-device AI solutions</li> <li>Integrate ML algorithms with microcontrollers, embedded controllers, and edge computing platforms</li> <li>Implement pipelines for model conversion, quantization, and hardware acceleration</li> <li>Collaborate with software, controls, and hardware engineers to deliver production-grade embedded AI systems</li> <li>Participate in prototyping, testing, benchmarking, and continuous improvement of embedded AI solutions</li> <li>Work with the global research teams to translate concepts into scalable prototypes for industrial automation</li> </ul> <p> </p> <p><strong>Your qualification:</strong></p> <ul> <li>Bachelor’s or Master’s degree in Computer Science, Electrical Engineering, Electronics, or related field</li> <li>5 – 10 years of hands-on experience in Embedded Systems and AI/ML development</li> <li>Strong programming proficiency in Python, C/C++, and embedded development</li> <li>Practical experiencewith embedded ML frameworks such as TensorFlow Lite, ONNX Runtime, Edge Impulse, or similar</li> <li>Solid understanding of real-time embedded systems, microcontroller architectures, and communication protocols</li> <li>Experience with model optimization techniques (quantization, pruning, hardware-specific acceleration) is a plus</li> <li>Familiarity with edge hardware platforms such as ARM Cortex, STM32, ESP32, NVIDIA Jetson, or similar is desirable</li> <li>Ability to work in a fast-paced research-driven environment with strong ownership and autonomy</li> </ul>